The Tool Desk
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Start with the exact model and tag
A model family name does not tell you how much memory a particular download uses. Open the model’s Ollama library page and select the exact tag you intend to run. Tags can differ in parameter count, quantization, or other configuration details, so check the page’s stated size and configuration before downloading. For example, Ollama’s Llama 2 library page offers approximate RAM guidance for its model sizes, but those figures are not guarantees for every Ollama model.
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As a rough starting point, that page says 7B models generally require at least 8GB of RAM, 13B models at least 16GB, and 70B models at least 64GB. Treat these as broad guidance, not a sizing formula: your chosen tag, context, runtime, and concurrent applications can change what works.
Account for more than model weights
The downloaded model weights are only part of the memory picture. During inference, context and runtime needs consume additional memory, and the operating system and other applications need room too. GPU memory is not interchangeable with system RAM in every setup; if the model does not fit entirely in VRAM, the runtime and backend determine how it is handled, and performance may differ.
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Do not choose a configuration that uses every last gigabyte shown as available. Keep headroom for the prompt and conversation history you actually need, plus runtime overhead and ordinary desktop use. Ollama does not publish one universal memory table covering every model, quantization, context length, platform, and GPU, so the model’s live page and a run on your hardware are the useful checks.
Choose a quantization that balances memory and quality
Quantization reduces the precision used to store model weights, usually reducing memory use compared with a higher-precision version, with possible quality and performance trade-offs. Ollama’s Llama 2 page says, “By default, Ollama uses 4-bit quantization,” and notes that higher quantization levels require more memory. That statement is specific to the page’s guidance; check the exact tag for the model you want rather than assuming every family uses the same default or offers identical options.
If a model appears too large, look for a lower-memory quantized tag before ruling it out. Compare the available options for the same model and task, and avoid assuming that a smaller file will perform identically to a higher-precision version.
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Set context length for the task
Context is the prompt and conversation history the model can consider. A longer context can raise memory use substantially, so a model that starts successfully with a short prompt may not fit at the context length you need. Choose a context target based on real use: a brief question-and-answer session may not need the same history as a large codebase or long document.
Ollama’s examples show why context must be part of the calculation. In its September 23, 2025 scheduling post, Ollama reported Gemma 3 12B at 128k context using 21.4 GiB of VRAM on one NVIDIA GeForce RTX 4090. That is a particular model, context, and machine configuration—not a universal minimum for Gemma 3 12B. For a coding-tool setup, Ollama’s January 23, 2026 post shows GLM-4.7-Flash at 64,000 tokens with approximately 23 GB VRAM required. This example likewise applies to the stated setup, not every use of that model.
Check the right memory for your platform
Windows or Linux PCs with a discrete GPU
Compare the model’s likely needs with both system RAM and the GPU’s VRAM. Ollama’s June 5, 2026 post describes Ollama 0.30, improved GGUF compatibility through llama.cpp, and Vulkan enabled by default to widen GPU acceleration across AMD and Intel devices. It also reports testing Gemma 4 26B with Q4_K_M on an NVIDIA RTX 5090. These details show that GPU and backend support are evolving; they do not establish a minimum GPU for Gemma 4 26B or guarantee that a particular card will run every model.
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Apple Silicon Macs
Apple Silicon uses unified memory shared by the system and workloads, so compare a model’s demands with the Mac’s available unified memory rather than treating it as a separate VRAM pool. In its March 30, 2026 MLX preview post, Ollama recommends a Mac with more than 32GB of unified memory for the described Qwen3.5-35B-A3B coding workflow. That recommendation is for that preview example, not a general minimum for Ollama on a Mac.
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Vision and multimodal models
Task and architecture matter as much as parameter count. Ollama’s November 6, 2024 Llama 3.2 Vision announcement says its 11B version requires at least 8GB of VRAM and its 90B version at least 64GB. Those requirements apply to those specific variants; they should not be applied to other models, including text-only models with similar parameter counts.
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- Open the model page: find the exact model and tag in the Ollama model library, then note its stated download size and quantization.
- Choose a realistic context: use the context target your task needs rather than the maximum listed or a value copied from another setup.
- Leave headroom: account for weights, context, runtime overhead, and other apps in system memory and, where applicable, GPU memory.
- Run the model on your computer: start with the intended prompt and context, then check actual allocation with
ollama ps. Ollama’s September 2025 scheduling post says its newer engine measures memory needs for supported models rather than relying only on estimates; support and behavior depend on the model and runtime. - Adjust one factor at a time: if the model fails to load or runs out of memory, try a smaller quantized tag or shorter context, close memory-heavy apps, or choose a smaller model. Recheck allocation after each change.
Compare options by fit and task
| What to compare | Why it matters |
|---|---|
| Memory headroom | Weight size is not total runtime use; context, overhead, and other applications need memory too. |
| Context target | Long prompts and histories can substantially increase memory demands, especially for coding workflows. |
| Quantization | A more memory-efficient option may fit better, while precision and quality trade-offs can vary by model and tag. |
| Task capability | A smaller model may fit more comfortably but may not suit a coding, vision, or tool-using task as well. |
| Platform and backend | Discrete GPU acceleration, system RAM, and Apple unified memory behave differently; actual allocation depends on the supported runtime path. |
| Measured behavior | A local run with your intended context is a better fit check than extrapolating from another person’s hardware example. |
When to consider a hardware upgrade
First try a model that fits your current system, an available lower-memory quantization, or a shorter context. Consider an upgrade only if those choices do not meet the capability or context your work requires. More VRAM may broaden GPU-accelerated options, but Ollama’s RTX 4090 and RTX 5090 examples are not evidence that either card is necessary or suitable for every user. Check the exact model, tag, context, and supported backend before buying hardware.
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